Challenges arise in querying and characterizing blockchain transactions and nodes in order to engage in blockchain-based analytics. Available tools are not designed for analytical workloads. Existing open-source and other tools are designed for nodes, developers writing blockchain contracts, miners, exchanges, and so on. These types of users require a fundamentally different access pattern than those required for analytical use-cases. Different types of blockchain data need to be stored, structured, and accessed differently. Addresses, transfer style transactions, contracts, and contract method calls are fundamentally different entities. Blockchains are not as simple as “user A transferred ‘X’ to user B.” All of these entities, depending on the access pattern and use-case, require unique storage choices.
Traditional data mining and machine learning approaches are insufficient to characterize nodes and transactions. Earlier approaches use traditional clustering algorithms coupled with some ground-truth tags to identify groups of addresses to illuminate behaviors. This probabilistic-only approach leads to a false representation of on-chain behaviors because there is typically insufficient data and unknown categories of classification to build a reliable probabilistic model, resulting in extremely high error rates. More accurate techniques for characterizing blockchain behaviors are needed.
Another challenge introduced by the trading of these crypto-assets relates to the responsibility for safeguarding the assets on behalf of clients. Enterprises interacting with blockchain-based products need to secure balances of digital assets. This means entrusting an individual(s) to safeguard private keys, which are subject to theft or loss. Traditional financial entities may not be qualified to securely custody these assets and, even if they are qualified, from a regulatory perspective they may not be legally entitled to this responsibility. In conventional schemes, asset custody services are provided by a third-party trust/service. In either case this simply results to pushing the problem onto a separate party (often the client themselves), and many clients do not want to be responsible for securing their own assets.
Further, conventional asset management systems use well-known statistics and financial results to value assets that are directly related to the assets' performance. For example, the expected performance or value of an equity may be based on historical earnings performance (or expected performance) of the company issuing the equity, quantitative analyses of the company, market capitalization, price to earnings ratios, and other technical data. Pricing for groups of assets (e.g., “funds” such as mutual funds, ETFs, etc.) may aggregate the values of the component assets that comprise the fund. Analysts use various combinations of these metrics to arrive at a perceived “value” of an asset.
While some of these same metrics may be used to value crypto-assets, many do not exist or are not as accurate as they may be for traditional assets. Moreover, the underlying structures and activities on which the crypto-assets are based are quite different. As a result, there is a need for new techniques for valuing these assets.